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Neural Architecture Search

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하위 태스크: 1
평가표: 26
보존된 평가 기록: 424

Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.

Image Credit : NAS with Reinforcement Learning

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/yhangchen/op-gfn

공식 코드

Order-Preserving GFlowNets

https://github.com/yilunliao/vit-search

공식 코드

Searching for Efficient Multi-Stage Vision Transformers

https://github.com/yitewang/metantk-nas

공식 코드

Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning

https://github.com/yjump/NAS-for-CSMRI

공식 코드

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction

https://github.com/yochaiz/Slimmable

공식 코드

Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks

https://github.com/yochaiz/darts-UNIQ

공식 코드

Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks

https://github.com/yoichii/nashpobench2api

공식 코드

NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters

https://github.com/yoniaflalo/knapsack_pruning

공식 코드

Knapsack Pruning with Inner Distillation

https://github.com/yu-zhewen/svd-nas

공식 코드

SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search

https://github.com/yucornetto/CAKES

공식 코드

CAKES: Channel-wise Automatic KErnel Shrinking for Efficient 3D Networks

https://github.com/yuezuegu/UBoostNAS

공식 코드

U-Boost NAS: Utilization-Boosted Differentiable Neural Architecture Search

https://github.com/yuhuixu1993/PC-DARTS

공식 코드

PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

https://github.com/yukang2017/RENAS

공식 코드

Reinforced Evolutionary Neural Architecture Search

https://github.com/yygle/HybridFormer

공식 코드

HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism

https://github.com/yyj1ang/mfpsnet

공식 코드

Multi-Prior Learning via Neural Architecture Search for Blind Face Restoration

https://github.com/yyxiongzju/RCNet

공식 코드

Resource Constrained Neural Network Architecture Search: Will a Submodularity Assumption Help?

https://github.com/zehao-dong/pace

공식 코드

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs

https://github.com/zerocostptnas/zerocost_operation_score

공식 코드

Zero-Cost Operation Scoring in Differentiable Architecture Search

https://github.com/zhengsh123/dclp

공식 코드

DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning

https://github.com/zhengxiawu/rethinking_performance_estimation_in_NAS

공식 코드

Rethinking Performance Estimation in Neural Architecture Search

https://github.com/zhoubenjia/RAAR3DNet

공식 코드

Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition

https://github.com/zhoudaquan/rethinking_bottleneck_design

공식 코드

Rethinking Bottleneck Structure for Efficient Mobile Network Design

https://github.com/zhuang-group/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/zhuxunyu/OPP-DARTS

공식 코드

Operation-level Progressive Differentiable Architecture Search

https://github.com/zifuwanggg/mrf-unets

공식 코드

MRF-UNets: Searching UNet with Markov Random Fields

https://github.com/zip-group/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/ziplab/spvit

공식 코드

Pruning Self-attentions into Convolutional Layers in Single Path

https://github.com/zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization

공식 코드

Simultaneous Weight and Architecture Optimization for Neural Networks

https://github.com/zj-zhang/BioNAS-pub

공식 코드

Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge

https://github.com/zj-zhang/elektrum

공식 코드

Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions

https://gitlab.com/cioflan/ms-ranas

공식 코드

MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search

https://gitlab.com/d-towers/nas-reproducibility

공식 코드

Long-term Reproducibility for Neural Architecture Search

https://gitlab.com/emil-benedykciuk/u-net-darts-tensorflow

공식 코드

Implantable Adaptive Cells: differentiable architecture search to improve the performance of any trained U-shaped network

https://gitlab.com/viper-purdue/lic-pruning

공식 코드

Structured Pruning and Quantization for Learned Image Compression

http://github.com//xingchenwan/cell-based-NAS-analysis

On Redundancy and Diversity in Cell-based Neural Architecture Search

https://bitbucket.org/act-lab/release

Proximal Policy Optimization Algorithms

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